






import pyarrow.parquet as pq
import pytest

from datasets import Audio, Dataset, DatasetDict, Features, NamedSplit, Sequence, Value, config
from datasets.features.image import Image
from datasets.io.parquet import ParquetDatasetReader, ParquetDatasetWriter, get_writer_batch_size

from ..utils import assert_arrow_memory_doesnt_increase, assert_arrow_memory_increases




def       _SCREAMING_SNAKE_CASE (							__snake_case   ,      __snake_case )				->						Optional[Any]:
       assert isinstance(__snake_case   ,      __snake_case )
       assert dataset.num_rows == 4
       assert dataset.num_columns == 3
       assert dataset.column_names == ["col_1", "col_2", "col_3"]
       for feature, expected_dtype in expected_features.items():
              assert dataset.features[feature].dtype == expected_dtype




@pytest.mark.parametrize("""keep_in_memory"""   ,      [False, True] )
def       _SCREAMING_SNAKE_CASE (							__snake_case   ,      __snake_case   ,      __snake_case )				->						Optional[Any]:
       _UpperCAmelCase							=    tmp_path / """cache"""
       _UpperCAmelCase							=    {"""col_1""": """string""", """col_2""": """int64""", """col_3""": """float64"""}
       with assert_arrow_memory_increases() if keep_in_memory else assert_arrow_memory_doesnt_increase():
              _UpperCAmelCase							=    ParquetDatasetReader(__snake_case   ,      cache_dir=__snake_case   ,      keep_in_memory=__snake_case ).read()
       _check_parquet_dataset(__snake_case   ,      __snake_case )




@pytest.mark.parametrize(
    """features"""   ,      [
        None,
        {"""col_1""": """string""", """col_2""": """int64""", """col_3""": """float64"""},
        {"""col_1""": """string""", """col_2""": """string""", """col_3""": """string"""},
        {"""col_1""": """int32""", """col_2""": """int32""", """col_3""": """int32"""},
        {"""col_1""": """float32""", """col_2""": """float32""", """col_3""": """float32"""},
    ]   ,      )
def       _SCREAMING_SNAKE_CASE (							__snake_case   ,      __snake_case   ,      __snake_case )				->						Optional[int]:
       _UpperCAmelCase							=    tmp_path / """cache"""
       _UpperCAmelCase							=    {"""col_1""": """string""", """col_2""": """int64""", """col_3""": """float64"""}
       _UpperCAmelCase							=    features.copy() if features else default_expected_features
       _UpperCAmelCase							=    (
           Features({feature: Value(__snake_case ) for feature, dtype in features.items()} ) if features is not None else None
       )
       _UpperCAmelCase							=    ParquetDatasetReader(__snake_case   ,      features=__snake_case   ,      cache_dir=__snake_case ).read()
       _check_parquet_dataset(__snake_case   ,      __snake_case )




@pytest.mark.parametrize("""split"""   ,      [None, NamedSplit("""train""" ), """train""", """test"""] )
def       _SCREAMING_SNAKE_CASE (							__snake_case   ,      __snake_case   ,      __snake_case )				->						Any:
       _UpperCAmelCase							=    tmp_path / """cache"""
       _UpperCAmelCase							=    {"""col_1""": """string""", """col_2""": """int64""", """col_3""": """float64"""}
       _UpperCAmelCase							=    ParquetDatasetReader(__snake_case   ,      cache_dir=__snake_case   ,      split=__snake_case ).read()
       _check_parquet_dataset(__snake_case   ,      __snake_case )
       assert dataset.split == split if split else "train"




@pytest.mark.parametrize("""path_type"""   ,      [str, list] )
def       _SCREAMING_SNAKE_CASE (							__snake_case   ,      __snake_case   ,      __snake_case )				->						Any:
       if issubclass(__snake_case   ,      __snake_case ):
              _UpperCAmelCase							=    parquet_path
       elif issubclass(__snake_case   ,      __snake_case ):
              _UpperCAmelCase							=    [parquet_path]
       _UpperCAmelCase							=    tmp_path / """cache"""
       _UpperCAmelCase							=    {"""col_1""": """string""", """col_2""": """int64""", """col_3""": """float64"""}
       _UpperCAmelCase							=    ParquetDatasetReader(__snake_case   ,      cache_dir=__snake_case ).read()
       _check_parquet_dataset(__snake_case   ,      __snake_case )




def       _SCREAMING_SNAKE_CASE (							__snake_case   ,      __snake_case   ,      __snake_case=("train",) )				->						List[str]:
       assert isinstance(__snake_case   ,      __snake_case )
       for split in splits:
              _UpperCAmelCase							=    dataset_dict[split]
              assert dataset.num_rows == 4
              assert dataset.num_columns == 3
              assert dataset.column_names == ["col_1", "col_2", "col_3"]
              for feature, expected_dtype in expected_features.items():
                     assert dataset.features[feature].dtype == expected_dtype




@pytest.mark.parametrize("""keep_in_memory"""   ,      [False, True] )
def       _SCREAMING_SNAKE_CASE (							__snake_case   ,      __snake_case   ,      __snake_case )				->						Tuple:
       _UpperCAmelCase							=    tmp_path / """cache"""
       _UpperCAmelCase							=    {"""col_1""": """string""", """col_2""": """int64""", """col_3""": """float64"""}
       with assert_arrow_memory_increases() if keep_in_memory else assert_arrow_memory_doesnt_increase():
              _UpperCAmelCase							=    ParquetDatasetReader(
                  {"""train""": parquet_path}   ,      cache_dir=__snake_case   ,      keep_in_memory=__snake_case ).read()
       _check_parquet_datasetdict(__snake_case   ,      __snake_case )




@pytest.mark.parametrize(
    """features"""   ,      [
        None,
        {"""col_1""": """string""", """col_2""": """int64""", """col_3""": """float64"""},
        {"""col_1""": """string""", """col_2""": """string""", """col_3""": """string"""},
        {"""col_1""": """int32""", """col_2""": """int32""", """col_3""": """int32"""},
        {"""col_1""": """float32""", """col_2""": """float32""", """col_3""": """float32"""},
    ]   ,      )
def       _SCREAMING_SNAKE_CASE (							__snake_case   ,      __snake_case   ,      __snake_case )				->						Optional[Any]:
       _UpperCAmelCase							=    tmp_path / """cache"""
       _UpperCAmelCase							=    {"""col_1""": """string""", """col_2""": """int64""", """col_3""": """float64"""}
       _UpperCAmelCase							=    features.copy() if features else default_expected_features
       _UpperCAmelCase							=    (
           Features({feature: Value(__snake_case ) for feature, dtype in features.items()} ) if features is not None else None
       )
       _UpperCAmelCase							=    ParquetDatasetReader({"""train""": parquet_path}   ,      features=__snake_case   ,      cache_dir=__snake_case ).read()
       _check_parquet_datasetdict(__snake_case   ,      __snake_case )




@pytest.mark.parametrize("""split"""   ,      [None, NamedSplit("""train""" ), """train""", """test"""] )
def       _SCREAMING_SNAKE_CASE (							__snake_case   ,      __snake_case   ,      __snake_case )				->						List[Any]:
       if split:
              _UpperCAmelCase							=    {split: parquet_path}
       else:
              _UpperCAmelCase							=    """train"""
              _UpperCAmelCase							=    {"""train""": parquet_path, """test""": parquet_path}
       _UpperCAmelCase							=    tmp_path / """cache"""
       _UpperCAmelCase							=    {"""col_1""": """string""", """col_2""": """int64""", """col_3""": """float64"""}
       _UpperCAmelCase							=    ParquetDatasetReader(__snake_case   ,      cache_dir=__snake_case ).read()
       _check_parquet_datasetdict(__snake_case   ,      __snake_case   ,      splits=list(path.keys() ) )
       assert all(dataset[split].split == split for split in path.keys() )




def       _SCREAMING_SNAKE_CASE (							__snake_case   ,      __snake_case )				->						Dict:
       _UpperCAmelCase							=    ParquetDatasetWriter(__snake_case   ,      tmp_path / """foo.parquet""" )
       assert writer.write() > 0
       _UpperCAmelCase							=    pq.ParquetFile(tmp_path / """foo.parquet""" )
       _UpperCAmelCase							=    pf.read()
       assert dataset.data.table == output_table




def       _SCREAMING_SNAKE_CASE (							__snake_case   ,      __snake_case )				->						List[Any]:
       _UpperCAmelCase							=    str(shared_datadir / """test_image_rgb.jpg""" )
       _UpperCAmelCase							=    {"""image""": [image_path]}
       _UpperCAmelCase							=    Features({"""image""": Image()} )
       _UpperCAmelCase							=    Dataset.from_dict(__snake_case   ,      features=__snake_case )
       _UpperCAmelCase							=    ParquetDatasetWriter(__snake_case   ,      tmp_path / """foo.parquet""" )
       assert writer.write() > 0

       _UpperCAmelCase							=    Dataset.from_parquet(str(tmp_path / """foo.parquet""" ) )
       assert dataset.features == reloaded_dataset.features

       _UpperCAmelCase							=    ParquetDatasetReader(str(tmp_path / """foo.parquet""" )   ,      streaming=__snake_case ).read()
       assert dataset.features == reloaded_iterable_dataset.features





@pytest.mark.parametrize(
    """feature, expected"""   ,      [
        (Features({"""foo""": Value("""int32""" )} ), None),
        (Features({"""image""": Image(), """foo""": Value("""int32""" )} ), config.PARQUET_ROW_GROUP_SIZE_FOR_IMAGE_DATASETS),
        (Features({"""nested""": Sequence(Audio() )} ), config.PARQUET_ROW_GROUP_SIZE_FOR_AUDIO_DATASETS),
    ]   ,      )
def       _SCREAMING_SNAKE_CASE (							__snake_case   ,      __snake_case )				->						List[Any]:
       assert get_writer_batch_size(__snake_case ) == expected

"""simple docstring"""






def __magic_name__	(				_lowerCamelCase:		Tuple,					_lowerCamelCase:		Any			)					->						Optional[Any]:

	'''simple docstring'''




	lowerCAmelCase												=  [1]
	for i in range(2,					_lowerCamelCase			):
		factorials.append(factorials[-1] * i			)
	assert 0 <= k < factorials[-1] * n, "k out of bounds"

	lowerCAmelCase												=  []
	lowerCAmelCase												=  list(range(_lowerCamelCase			)			)

	# Find permutation
	while factorials:
		lowerCAmelCase												=  factorials.pop()
		lowerCAmelCase							,       lowerCAmelCase												=  divmod(_lowerCamelCase,					_lowerCamelCase			)
		permutation.append(elements[number]			)
		elements.remove(elements[number]			)
	permutation.append(elements[0]			)

	return permutation


if __name__ == "__main__":
					import doctest

					doctest.testmod()


